brewing_materials <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-03-31/brewing_materials.csv')
beer_taxed <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-03-31/beer_taxed.csv')
brewer_size <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-03-31/brewer_size.csv')
beer_states <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-03-31/beer_states.csv')
head(brewing_materials)## # A tibble: 6 x 9
## data_type material_type year month type month_current month_prior_year
## <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl>
## 1 Pounds o… Grain Produc… 2008 1 Malt… 374165152 365300134
## 2 Pounds o… Grain Produc… 2008 1 Corn… 57563519 41647092
## 3 Pounds o… Grain Produc… 2008 1 Rice… 72402143 81050102
## 4 Pounds o… Grain Produc… 2008 1 Barl… 3800844 2362162
## 5 Pounds o… Grain Produc… 2008 1 Whea… 1177186 1195381
## 6 Pounds o… Total Grain … 2008 1 Tota… 509108844 491554871
## # … with 2 more variables: ytd_current <dbl>, ytd_prior_year <dbl>head(beer_taxed)## # A tibble: 6 x 10
## data_type tax_status year month type month_current month_prior_year
## <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl>
## 1 Barrels … Totals 2008 1 Prod… 16211480 15880125
## 2 Barrels … Taxable 2008 1 In b… 13222104 12824278
## 3 Barrels … Taxable 2008 1 In b… 1371239 1357372
## 4 Barrels … Taxable 2008 1 Tax … 7426 8419
## 5 Barrels … Sub Total… 2008 1 Sub … 14600769 14190069
## 6 Barrels … Tax Free 2008 1 For … 262985 268473
## # … with 3 more variables: ytd_current <dbl>, ytd_prior_year <dbl>,
## # tax_rate <chr>head(brewer_size)## # A tibble: 6 x 6
## year brewer_size n_of_brewers total_barrels taxable_removals total_shipped
## <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 2009 6,000,001 Bar… 18 171232882. 159643984. 3639970
## 2 2009 1,000,001 to … 4 9970404. 9592723. 14548
## 3 2009 500,001 to 1,… 7 4831386. 4535659. 21563
## 4 2009 100,001 to 50… 27 5422156. 4469289. 128000
## 5 2009 60,001 to 100… 19 1501977. 1224618. 95732
## 6 2009 30,001 to 60,… 32 1412245. 1233959. 14369head(beer_states)## # A tibble: 6 x 4
## state year barrels type
## <chr> <dbl> <dbl> <chr>
## 1 AK 2008 2068. On Premises
## 2 AK 2009 2264. On Premises
## 3 AK 2010 1929. On Premises
## 4 AK 2011 2251. On Premises
## 5 AK 2012 2312. On Premises
## 6 AK 2013 2156. On Premisesbeer_states %>% #only viewing barrels in New Jersey
filter(state == "NJ") -> nj_beer
head(nj_beer) #view dataframe## # A tibble: 6 x 4
## state year barrels type
## <chr> <dbl> <dbl> <chr>
## 1 NJ 2008 4958. On Premises
## 2 NJ 2009 4945. On Premises
## 3 NJ 2010 5642. On Premises
## 4 NJ 2011 5500. On Premises
## 5 NJ 2012 5385. On Premises
## 6 NJ 2013 5536. On Premiseshchart(nj_beer, "line", hcaes(x = year, y = barrels, group = type)) %>%
hc_title(text = "Production of Beer Barrels in New Jersey") -> plot_beer## Warning: `parse_quosure()` is deprecated as of rlang 0.2.0.
## Please use `parse_quo()` instead.
## This warning is displayed once per session.plot_beerThis TidyTuesday was lot of fun to make, especially since it was interactive. The highcharter website had plenty of examples that made it quite easy to learn. I was actually expecting it to be more complex, but it was quite similar to plotting in ggplot. It made me realize that once you know the foundation, you use that as a base to learn new concepts.
nj_beer %>%
mutate(year = as.character(year)) %>%
ggplot(aes(x = year, y = barrels, group = type, color = type)) +
geom_point() + geom_line() +
ggtitle("Production of Beer Barrels in New Jersey") +
labs(x = "Year", y = "Barrels", color = "Type") +
theme_classic() +
theme(legend.position = "bottom")-> p2
ggplotly(p2)An alternative to highcharter is plotly. It is more straight forward than highcharter, especially if you are already familiar with ggplot. It is also more interactive than highcharter. Nonetheless, both are options for interactive data figures.